_:n5b093bc754af4c7a84698a5bf60ea9b3xb7
<http://schema.org/description>
"Variational Inference is a powerful tool in the Bayesian modeling toolkit,\nhowever, its effectiveness is determined by the expressivity of the utilized\nvariational distributions in terms of their ability to match the true posterior\ndistribution. In turn, the expressivity of the variational family is largely\nlimited by the requirement of having a tractable density function. To overcome\nthis roadblock, we introduce a new family of variational upper bounds on a\nmarginal log density in the case of hierarchical models (also known as latent\nvariable models). We then give an upper bound on the Kullback-Leibler\ndivergence and derive a family of increasingly tighter variational lower bounds\non the otherwise intractable standard evidence lower bound for hierarchical\nvariational distributions, enabling the use of more expressive approximate\nposteriors. We show that previously known methods, such as Hierarchical\nVariational Models, Semi-Implicit Variational Inference and Doubly\nSemi-Implicit Variational Inference can be seen as special cases of the\nproposed approach, and empirically demonstrate superior performance of the\nproposed method in a set of experiments."
<https://deepai.org/organization/hse-university>